Morphology for jet classification
Morphology for jet classification
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射流分类的形态学
DOI：
10.1103/physrevd.105.014004
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发表时间：
2022
影响因子：
5
通讯作者：
Nojiri Mihoko M.
中科院分区：
文献类型：
--
作者：
Lim Sung Hak;Nojiri Mihoko M.
介绍了一种基于神经网络分析像素化射流图像的闵可夫斯基函数（MFs）的射流标记器。粒子数是二值图像的几何度量，是粒子数的一种概括，粒子数是射流标记中的一个重要量。它们的膨胀变化编码了喷流成分在不同角度尺度上的几何结构。我们明确地表明，使用mf和膨胀的分析可以被认为是一个约束卷积神经网络（CNN）。相反，CNN可以在大型网络的限制下对mf进行建模。我们展示了一个例子，在隐谷场景的半可见射流标记中，CNN决策边界与mf值有很强的相关性。MFs独立于射流物理中常用的红外和共线（IRC）安全观测。我们将这种形态分析与irc安全关系网络相结合，该网络模拟两点能量相关性。虽然最终的网络使用受限的输入参数，但它显示出与CNN相当的暗射流和顶射流标记性能。当可用数据有限时，该体系结构具有显著的计算优势。结果表明，在训练样本较少的情况下，其标注性能比CNN要好得多。我们还定性地讨论了它们的部淋模型依赖关系。结果表明，MFs可以作为喷气机irc -不安全特征空间的有效参数化方法。
We introduce a jet tagger based on a neural network analyzing the Minkowski functionals (MFs) of pixelated jet images. The MFs are geometric measures of binary images, and they can be regarded as a generalization of the particle multiplicity, which is an important quantity in jet tagging. Their changes by dilation encode the jet constituents’ geometric structures that appear at various angular scales. We explicitly show that this analysis using the MFs and dilation can be considered a constrained convolutional neural network (CNN). Conversely, CNN could model the MFs in the limit of a large network. We show an example that the CNN decision boundary correlates strongly with the value of MFs in semivisible jet tagging of a hidden valley scenario. The MFs are independent of the infrared and collinear (IRC)-safe observables commonly used in jet physics. We combine this morphological analysis with an IRC-safe relation network which models two-point energy correlations. While the resulting network uses constrained input parameters, it shows comparable dark jet and top jet tagging performances to the CNN. The architecture has significant computational advantages when the available data is limited. We show that its tagging performance is much better than that of the CNN with a small number of training samples. We also qualitatively discuss their parton shower model dependency. The results suggest that the MFs can be an efficient parametrization of the IRC-unsafe feature space of jets.